Method for evaluating gust waveforms and flow fields and control system therefor

Abstract

The application discloses a gust waveform and flow field evaluation method and a control system thereof, and relates to the technical field of wind tunnel test. The method comprises the following steps: obtaining an experimental gust waveform and an experimental gust flow field measured by a space experiment in a target area; when the experimental gust waveform is evaluated, an ideal gust waveform is obtained by fitting based on a least square method, a time domain error and a frequency domain error of the experimental gust waveform and the ideal gust waveform are calculated, and a waveform distortion coefficient is obtained by dynamic weight fusion; when the experimental gust flow field is evaluated, gust signals of each measuring point are extracted and phase alignment is performed, a spatial average wave is calculated, and then a spatial time domain error, a spatial frequency domain error and a turbulence error are calculated in combination with the experimental gust flow field, and a flow field distortion coefficient is obtained by dynamic weight fusion. The application can realize unified quantitative and multi-dimensional evaluation of different gust waveforms, and provides reliable quantitative basis for analysis, regulation and control of complex gust flow fields and optimization of test conditions.

Description

Technical Field

[0001] This application relates to the field of wind tunnel testing technology, and in particular to a method for evaluating gust waveforms and flow fields and its control system. Background Technology

[0002] Wind tunnel gust testing is an important tool for studying the aerodynamic response of aircraft and other structures in unsteady flow. Accurately evaluating the gust waveform quality and flow field uniformity is crucial for ensuring the validity and repeatability of experimental data. Currently, for gust waveform quality, existing methods typically obtain velocity-time histories at single points or a small number of measurement points, describing the waveform based on a limited number of characteristic parameters such as peak velocity and rise time. Some methods also incorporate spectral analysis or correlation analysis to determine the presence of high-frequency disturbances in the gust signal. For gust flow field uniformity, existing methods largely rely on multi-point measurement data, evaluating it using statistical indicators such as spatially averaged velocity and turbulence intensity. These methods are feasible for steady flow field testing and qualitative assessment.

[0003] However, in the unsteady flow scenario of gust testing, traditional methods have obvious shortcomings: (1) Waveform quality evaluation lacks a unified quantitative standard. Existing methods rely on manually selecting a small number of characteristic parameters. The key characteristics corresponding to different gust forms (such as sinusoidal gusts, 1-cos gusts or other complex gusts) are inconsistent. Single or a small number of characteristics are difficult to fully reflect waveform distortion, and the evaluation results are easily affected by subjective experience. (2) The evaluation of flow field uniformity is difficult to take into account the coupled effects of multiple factors. Existing methods are mostly based on the analysis of instantaneous cross-sectional data at a certain moment or the statistical indicators of a certain measuring point, ignoring the phase differences and spectral structure differences between different measuring points, and it is also difficult to take into account the comprehensive effects of time domain, frequency domain and turbulent random disturbance at the same time. Furthermore, during actual wind tunnel operation, there are often amplitude errors, phase deviations, and spectral distortions between the generated gusts and the ideal waveforms. Wake interference or localized flow instability may also occur in the flow field. Current technologies lack a unified and widely applicable comprehensive evaluation method, making it difficult to quantitatively describe the degree of waveform distortion and flow field disturbance under different gust types, test conditions, and spatial regions. This also hinders the effective optimization and adjustment of gust generator parameters and the rational selection of test areas. Adjusting gust generator parameters (such as amplitude and motion period) mainly relies on repeated manual experiments, lacking a feedback optimization mechanism based on quantitative evaluation results, leading to low adjustment efficiency. Due to the lack of clear spatial quality criteria, the effective test area is difficult to accurately identify.

[0004] Therefore, how to provide a unified quantitative evaluation method applicable to different gust patterns, different test conditions and different spatial regions, and establish a parameter feedback adjustment mechanism based on the evaluation results, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this application is to provide a method and control system for evaluating gust waveforms and flow fields, which can realize unified quantitative and multi-dimensional evaluation of different gust waveforms, and provide reliable quantitative basis for the analysis, control and optimization of experimental conditions of complex gust flow fields.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for evaluating gust waveforms and flow fields, including: Obtain the experimental gust waveform and experimental gust flow field of the target area spatial experimental measurement.

[0007] When evaluating the experimental gust waveform: The experimental gust waveform was fitted using the least squares method to obtain the ideal gust waveform.

[0008] Calculate the time-domain error between the experimental gust waveform and the ideal gust waveform.

[0009] Calculate the frequency domain error between the experimental gust waveform and the ideal gust waveform.

[0010] Based on the time-domain error and the frequency-domain error, a waveform distortion coefficient is obtained by dynamic weight fusion; the waveform distortion coefficient is used to quantitatively evaluate the degree of distortion of the gust waveform.

[0011] When evaluating the experimental gust flow field: Based on the experimental gust flow field, gust signals from several measuring points were extracted, and the gust signals from each measuring point were phase-aligned to obtain the phase-aligned gust signals.

[0012] The spatially averaged wave is calculated based on the phase-aligned gust signal.

[0013] Calculate the spatial-temporal error of the experimental gust flow field.

[0014] Calculate the spatial frequency domain error of the experimental gust flow field.

[0015] Calculate the turbulence error of the experimental gust flow field.

[0016] The spatial time domain error, the spatial frequency domain error, and the turbulence error are dynamically weighted and fused to obtain the gust flow field distortion coefficient; the flow field distortion coefficient is used to quantitatively evaluate the spatial uniformity of the gust flow field.

[0017] Secondly, this application provides a control system for gust waveforms and flow fields, the control system comprising: The parameter input module is used to set the amplitude and motion cycle of the gust generator as adjustable control parameters.

[0018] The gust evaluation module is used to calculate the waveform distortion coefficient based on the gust waveform and flow field evaluation method described in the first aspect.

[0019] The parameter iterative adjustment module is used to iteratively adjust the amplitude and motion period based on the deviation between the waveform distortion coefficient and the preset target range, until both the waveform distortion coefficient and the gust ratio meet the preset target range.

[0020] The flow field evaluation module is used to calculate the gust flow field distortion coefficient based on the gust waveform and flow field evaluation method described in the first aspect, after the parameters meet the preset target range.

[0021] The region identification module is used to identify effective spatial regions that meet the distortion constraints based on the gust flow field distortion coefficient.

[0022] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method and control system for evaluating gust waveforms and flow fields. First, it acquires experimental gust waveforms and flow fields measured in a target area. When evaluating the experimental gust waveform, an ideal gust waveform is constructed using the least squares method. The deviation between the experimental and ideal gust waveforms is calculated in both the time and frequency domains. A dynamic weighted fusion method is used to obtain the waveform distortion coefficient, which serves as an evaluation index for gust waveform quality, quantitatively reflecting the degree of distortion in the experimentally measured gust waveform. This method effectively solves the technical problem of gust waveform quality under unsteady flow conditions relying on subjective experience and lacking a unified and objective quantitative evaluation standard. It achieves a comprehensive quantitative evaluation of the multi-dimensional characteristics of gust waveforms in the time and frequency domains. Furthermore, the dynamic weighted adjustment mechanism improves the adaptability and reliability of the evaluation results, thereby significantly enhancing the objectivity and universality of gust waveform quality assessment.

[0023] When evaluating the experimental gust flow field, this application extracts the gust signals from each measuring point and performs phase alignment, calculates the spatial average wave, and then comprehensively evaluates the deviation of each measuring point from the spatial average wave from three dimensions: time domain, frequency domain, and turbulence. An adaptive dynamic weight fusion method is used to obtain the flow field distortion coefficient, allowing the contributions of time domain error, frequency domain error, and turbulence error to the total error to automatically adjust with changes in gust intensity. Alternatively, weight adjustment parameters can be manually set according to the degree of error concern, ensuring the evaluation method has good rationality and flexibility under different gust types and intensities. This method achieves a multi-dimensional, quantitative, and accurate evaluation of the spatial uniformity of the gust flow field, simultaneously reflecting the consistency of waveform amplitude, spectral structure, and turbulence disturbance in spatial distribution. It overcomes the shortcomings of existing technologies in providing a unified quantitative description of the spatial uniformity of complex gust flow fields, thus providing a reliable technical basis for optimizing the spatial region of gust generators, determining the effective test area, and controlling the quality of gust flow fields. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is an application environment diagram of an evaluation method for gust waveforms and flow fields according to an embodiment of this application; Figure 2 A flowchart illustrating a method for evaluating gust waveforms and flow fields according to an embodiment of this application; Figure 3 This is a schematic diagram of the time-domain evaluation result of the gust waveform provided in an embodiment of this application; Figure 4 This is a schematic diagram of the frequency domain evaluation results of gust waveforms provided in an embodiment of this application; Figure 5 A schematic diagram illustrating the spatiotemporal evolution characteristics of a vertical sin gust flow field provided in an embodiment of this application; Figure 6 The time-domain error of the gust flow field provided in one embodiment of this application is in A diagram illustrating the change in direction; Figure 7 The frequency domain error of the gust flow field provided in one embodiment of this application is in A diagram illustrating the change in direction; Figure 8 The turbulence error of the gust flow field provided in one embodiment of this application is within A diagram illustrating the change in direction; Figure 9 The flow field distortion coefficient provided in one embodiment of this application is A diagram illustrating the change in direction; Figure 10 A flowchart of a gust generator parameter adjustment system based on an evaluation method provided in an embodiment of this application; Figure 11 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] The evaluation method for gust waveforms and flow fields provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send the experimental gust waveform and experimental gust flow field measured in the target area to server 104. After receiving the experimental gust waveform and experimental gust flow field, server 104, when evaluating the experimental gust waveform, fits it using the least squares method to obtain an ideal gust waveform; calculates the time-domain error between the experimental gust waveform and the ideal gust waveform; calculates the frequency-domain error between the experimental gust waveform and the ideal gust waveform; and, based on the time-domain error and the frequency-domain error, uses dynamic weight fusion to obtain the waveform distortion. The waveform distortion coefficient is used to quantify and evaluate the degree of distortion of the gust waveform. When evaluating the experimental gust flow field, based on the experimental gust flow field, gust signals from several measuring points are extracted, and the gust signals from each measuring point are phase-aligned to obtain phase-aligned gust signals. Based on the phase-aligned gust signals, the spatial average wave is calculated. The spatial time-domain error of the experimental gust flow field is calculated. The spatial frequency-domain error of the experimental gust flow field is calculated. The turbulence error of the experimental gust flow field is calculated. The spatial time-domain error, the spatial frequency-domain error, and the turbulence error are fused using dynamic weights to obtain the gust flow field distortion coefficient. The flow field distortion coefficient is used to quantify and evaluate the spatial uniformity of the gust flow field. The server 104 can feed back the obtained waveform distortion coefficient and flow field distortion coefficient to the terminal 102. In addition, in some embodiments, the evaluation method of gust waveform and flow field can also be implemented by the server 104 or the terminal 102 separately. For example, the terminal 102 can directly perform relevant operations on the experimental gust waveform and experimental gust flow field measured in the target area space, or the server 104 can obtain the experimental gust waveform and experimental gust flow field measured in the target area space from the data storage system and perform relevant operations on the experimental gust waveform and experimental gust flow field measured in the target area space.

[0029] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0030] In one exemplary embodiment, such as Figure 2 As shown, a method for evaluating gust waveforms and flow fields is provided. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the following steps are included.

[0031] Obtain the experimental gust waveform and experimental gust flow field of the target area spatial experimental measurement.

[0032] A1. When evaluating the experimental gust waveform.

[0033] A2. The experimental gust waveform was fitted using the least squares method to obtain the ideal gust waveform.

[0034] Specifically, taking the sine waveform as an example, the number of measurement points is: After obtaining the sine waveform results through experimental measurement, the measurement data is first dimensionless, and then... This represents the change in vertical velocity, where This represents the magnitude of the vertical velocity. The incoming flow velocity; The ordinate parameters corresponding to the original measurement data ; Time is dimensionless; where For time; This represents the cycle of the blades' movement in the gust generator, and also the cycle of the gust waveform generated in the experiment. Corresponding x-axis parameter To explore the similarity between the measured gust waveform and the standard sine function, the smoothed measurement results were fitted with a sine using the least squares method, with the aim of finding the theoretical sine function that is most similar to the measured data.

[0035] The functional expression for fitting the sine waveform is: (1); in, The ordinate of the ideal gust waveform obtained by least squares fitting; It is the amplitude; It is angular frequency; It is the first The x-coordinates of each experimental measurement point; It is the initial phase; It is the offset (balance position).

[0036] residual for: (2); in, The vertical axis represents the experimental measurement results (i.e., the experimental gust waveform).

[0037] The goal of the least squares method is to minimize the sum of squared residuals. The objective function is: (3); By solving The minimum value can be obtained ,in The frequency of the gust generator's motion is consistent with that of the wind turbine and is a known quantity. Using trigonometric expansion, the nonlinear problem can be transformed into a linear problem for solution. The expression for the sine fitting result.

[0038] A3. Calculate the time-domain error between the experimental gust waveform and the ideal gust waveform.

[0039] Specifically, the velocity amplitude error at each measurement point is calculated in the time domain, and the expression is as follows: (4); in, This represents the average value of the experimental measurement results.

[0040] For ease of comparison, the error is amplified by taking the square root. The expression for the time-domain error is as follows: (5); in, This represents the time-domain error.

[0041] A4. Calculate the frequency domain error between the experimental gust waveform and the ideal gust waveform.

[0042] In another exemplary embodiment of this application, in order to accurately calculate the frequency domain error between the experimental gust waveform and the ideal gust waveform, the following specific steps are included: The experimental gust waveform and the ideal gust waveform are subjected to discrete Fourier transform to obtain the experimental spectrum and the theoretical spectrum, respectively.

[0043] The frequency range is divided into low-frequency band, transition band, and high-frequency band. The error between the experimental spectrum and the theoretical spectrum in each frequency band is calculated to obtain the low-frequency band error, transition band error, and high-frequency band error.

[0044] The frequency domain error is obtained by summing the low-frequency band error, the transition band error, and the high-frequency band error and taking the square root.

[0045] The discrete Fourier transform of the experimental gust waveform and the ideal gust waveform is expressed as follows: (6); (7); in, The first experimental gust waveform One frequency component, The imaginary unit; The first ideal gust waveform Each frequency component.

[0046] The single-sided amplitude spectrum of experimental gust waveforms and ideal gust waveforms is defined as follows: (8); (9); in, This is the single-sided amplitude spectrum of the experimental gust waveform; This represents the single-sided amplitude spectrum of an ideal gust waveform.

[0047] The expression for the dimensionless frequency on the horizontal axis is: (10); in, For time step; It is a dimensionless frequency.

[0048] The error expression between the experimental spectrum and the theoretical spectrum in each frequency band is as follows: (11); in, This refers to the error in each frequency band within different frequency ranges; Theoretical spectrum; This is the experimental spectrum; The frequency range for each frequency band; This is the maximum frequency.

[0049] In one example of this application, to balance the main frequency component and high-frequency disturbance information, the spectrum error calculation range is selected as follows: This range can be adjusted according to evaluation requirements. Errors in different frequency bands are calculated separately to identify the key frequency bands most affected by gust waveform distortion. Fourier transforms are performed on the experimental and theoretical waveforms of sin and 1-cos gusts, and spectrum diagrams are plotted. The time-frequency error is very small. The error is smaller for sin gusts, but the error increases significantly for 1-cos gusts. At that time, the error of both types of gusts increased. The error was further reduced, so we selected... Method validation was performed. This was done to further compare error results across different frequency ranges.

[0050] The expression for the low-frequency band error is: (12); in, This is a low-frequency band error.

[0051] The expression for the time transition band error is: (13); in, This is due to excessive frequency band error.

[0052] The expression for the high-frequency band error is: (14); in, This is for high-frequency band error.

[0053] Therefore, the expression for the frequency domain error is: (15); in, This represents the frequency domain error.

[0054] A5. Based on the time-domain error and the frequency-domain error, a waveform distortion coefficient is obtained by dynamic weight fusion; the waveform distortion coefficient is used to quantitatively evaluate the degree of distortion of the gust waveform.

[0055] To enhance the evaluation capability of frequency domain error under different gust conditions, a frequency domain enhancement coefficient is introduced into the dynamic weighting expression. To ensure that the frequency domain weight increases at a rate that corresponds to the gust ratio, while maintaining the same order of magnitude between the weighted time domain and frequency domain errors, the following approach is adopted in the validation of the evaluation method: =5, It can be adjusted according to the characteristics of the evaluation data and evaluation needs, improving the flexibility and universality of the evaluation method. Waveform distortion coefficient Taking into account both time and frequency domain characteristics, the deviation between the fitted data and the original data was evaluated, and this deviation can be used as a standard metric to assess the degree of distortion of the gust waveforms generated in the experiment. The minimum value is 0. The smaller the value, the better the fit, meaning the experimentally generated gust waveform approximates the theoretical sine function more closely, and the better the waveform quality. The larger the value, the worse the waveform quality and the higher the degree of waveform distortion.

[0056] The expression for the waveform distortion coefficient is: (16); in, The waveform distortion coefficient; Compared to gusts of wind, , This represents the maximum absolute value of the gust speed change. For frequency domain enhancement coefficients; For time-domain error; This represents the frequency domain error.

[0057] The gust waveform evaluation method of this application can also be applied to evaluate the quality of other waveforms. Only the expression of the fitted curve needs to be modified. Taking the 1-cos gust waveform as an example, the fitted curve only needs to be modified as follows: (17); The quality of sin and 1-cos waveforms under the same gust generator control parameters was evaluated, with 100 measurement points.

[0058] When the gust generator has an amplitude of 9° and a cycle of 4.72s, it generates... Figure 3 The sin gust waveform shown on the left shows that the measured waveform matches the ideal waveform well. The time-domain error calculated using the waveform quality evaluation method is... The value is 0.0581, which is obtained after Fourier transform. Figure 4 The sin gust spectrum results shown on the left show that... f When the value is less than 2, the measured spectrum matches the ideal spectrum well; when the value is greater than 2, a small deviation appears. The calculated low-frequency band error... The error is 3.6141e-16, representing the overband error. The high-frequency error is 9.3278e-04. The value is 0.0024, consistent with the result in the figure. Total frequency domain error. The value is 0.0577. The gust waveform distortion coefficient is obtained after dynamic weighted averaging. The value is 0.0580, which is close to 0, indicating that the gust waveform quality is good and there is basically no distortion in the time and frequency domains.

[0059] In contrast, the 1-cos gust waveform generated under the same motion parameters is as follows: Figure 3 As shown on the right, a significant discrepancy was found between the measured waveform and the ideal waveform. The experimentally measured waveform exhibited obvious distortion at the peaks. The calculated time-domain error... The value is 0.2917, which is obtained after Fourier transform. Figure 4 The 1-cos gust spectrum results shown on the right show that... f When the value is less than 1, the measured spectrum matches the ideal spectrum well. When the value is greater than 1, a large deviation appears, followed by a decrease in the deviation. The calculated low-frequency band error is... The error is 8.4525e-22, indicating an overband error. The error is 0.0422, representing the high-frequency band error. A value of 0.0081 indicates that the frequency domain quality of 1-cos gusts is lower than that of sin gusts in both the transitional and high-frequency bands. The total frequency domain error of 1-cos gusts... The value is 0.2243. After dynamic weighted averaging, the gust waveform distortion coefficient is obtained. The value was 0.2561, indicating that the 1-cos gust waveform quality was worse and the distortion was more pronounced compared to the sin gust. By comparing the gust waveform distortion coefficients, the errors in the time and frequency domains can be considered, allowing for a comprehensive evaluation of the waveform quality of different types of gusts.

[0060] B1. When evaluating the experimental gust flow field.

[0061] B2. Based on the experimental gust flow field, extract gust signals from several measuring points, and perform phase alignment on the gust signals from each measuring point to obtain the phase-aligned gust signals.

[0062] Specifically, the selected target gust region is discretized into One point. Due to differences There is a phase difference at the location, so it is necessary to transform the gust signal at each point to the same phase. Let... As the reference position, record For any Location gust signal and designated Phase delay time between location gust signals This can be obtained through cross-correlation analysis between the two signals. Subsequently, Vertical gusts at location The transformed waveform is obtained by translating along the time axis. ,in Take 1 to , Take 1 to The transformed waveform and The gust waveforms at that location are in phase. Because... The value of does not affect the final gust waveform, therefore in this calculation Take 3.4 times the chord length of the gust generator blades The location.

[0063] B3. Calculate the spatial average wave based on the phase-aligned gust signal.

[0064] The expression for the spatially averaged wave is: (18); in, Indicates spatially averaged wave; represent The number of discrete points in the direction; represent The number of discrete points in the direction; and We took values ​​of 10 and 8 respectively, and it was verified that these values ​​were sufficient to ensure convergence of the results. i For any Location gust signal and designated Phase delay time between location gust signals.

[0065] B4. Calculate the spatial-temporal error of the experimental gust flow field.

[0066] Specifically, spatially, the gust waveform at any point Spatial average wave The time-domain deviation between the two time series can be quantified by the Eulerian distance between them, expressed as: (19); in, It can be understood as a cycle T The average difference between corresponding values ​​of two waveforms.

[0067] Define spatial-temporal error The expression is: (20); (twenty one); in, This refers to spatial-temporal errors; This represents the gust ratio of the space-averaged wave.

[0068] B5. Calculate the spatial frequency domain error of the experimental gust flow field.

[0069] Specifically, the frequency domain error of the gust flow field is similar to the frequency domain error calculation method in the gust waveform quality evaluation method, which calculates the gust waveform at any point in space. Spatial average wave The spectrum image is obtained by performing a Fourier transform. and The range of spectral error calculation is: Similarly, select The method was validated, and the error results were compared across different frequency ranges.

[0070] The spatial frequency domain error expression for each frequency band is as follows: (twenty two); in, Spatial error in each frequency band within different frequency ranges; The spectrum obtained from the gust signal; The spectrum obtained from the spatially averaged wave; For dimensionless frequency, The frequency range for each frequency band; This is the maximum frequency.

[0071] The expression for the spatiotemporal low-frequency band error is: (twenty three); in, This refers to low-frequency band error in space.

[0072] The expression for the spatiotemporal transition band error is: (twenty four); in, This refers to the spatial overband error.

[0073] The expression for the spacetime high-frequency band error is: (25); in, This refers to the high-frequency band error in space.

[0074] The spatial frequency domain error expression is: (26); in, This represents the spatial frequency domain error.

[0075] B6. Calculate the turbulence error of the experimental gust flow field.

[0076] Specifically, since the turbulence intensity in the velocity field varies with spatial location, a turbulence intensity error is introduced into the evaluation system for the uniformity of the gust flow field. First, the local turbulence intensity is calculated, expressed as: (27); (28); in, For local turbulence intensity; The average gust waveform for the selected local spatial range; This represents the standard deviation of the velocity fluctuation.

[0077] Relative to spatial average turbulence Turbulence error The expression is: (29); (30); in, This is for turbulence error; For local turbulence intensity; The space-average turbulence intensity is denoted as .

[0078] B7. The spatial time domain error, the spatial frequency domain error, and the turbulence error are dynamically weighted and fused to obtain the gust flow field distortion coefficient; the flow field distortion coefficient is used to quantitatively evaluate the spatial uniformity of the gust flow field.

[0079] The expression for the flow field distortion coefficient is: (31); in, The flow field distortion coefficient; This refers to spatial-temporal errors; The weights for spatial-temporal errors; For spatial frequency domain error; The weights for spatial frequency domain errors; This is for turbulence error; This represents the weight of the turbulence error.

[0080] The adaptive dynamic weighting expression based on gust ratio is: (32); (33); (34); in, and For weight adjustment parameters; This represents the gust ratio of the space-averaged wave.

[0081] When the gust ratio (or gust intensity) is small, the gust fluctuation amplitude is relatively small, and the main differences are reflected in the shape of the time-domain waveform, while the influence of frequency-domain characteristics and turbulence effects is relatively weak. As the gust ratio increases, the gust structure gradually becomes more complex, the high-frequency components are significantly enhanced, and the turbulence effect becomes more pronounced. Therefore, it is necessary to increase the weight of frequency-domain error and turbulence error in the total error. The dynamic weight constructed in this way is a physical-driven form, which ensures that the weight changes are consistent with the main influencing factors of gust characteristic changes, thereby enhancing the physical rationality of the evaluation results. In the adaptive dynamic weight expression... ， and The weight adjustment parameter is taken during method verification. , By adjusting the parameters and The value of can be manually adjusted according to the different levels of attention paid to various types of errors in different studies, making the method more flexible and adaptable in practical applications.

[0082] The average value is denoted as This is used as a global measure of the uniformity of gusts in a specific area. A smaller value indicates less distortion in the gust flow field and better uniformity. It can also be fixed. or The value is used to evaluate the change in the degree of flow field distortion along a certain direction. Here, the calculation process of the flow field distortion coefficient is introduced based on vertical gusts. The same method can also be used to calculate the flow field distortion coefficient of directional gusts and evaluate its spatial homogeneity.

[0083] The method for evaluating the uniformity of the gust flow field was validated when the gust generator amplitude was 9 and the motion period was 4.72s. The spatiotemporal evolution characteristics of the vertical sin gust flow field at the location are as follows: Figure 5 As shown. Figure 5 middle x-axis t / T For dimensionless time, the vertical axis is... The distance is dimensionless, and the color intensity represents the magnitude of the dimensionless vertical gust velocity. The results show that the uniformity of the vertical gust flow field is along... The direction changes significantly, as As the flow rate increases, the uniformity of the flow field first improves and then deteriorates.

[0084] Further calculations were performed for different operating conditions. Temporal error of gust flow field at location (e.g.) Figure 6 ), frequency domain error of gust flow field (such as Figure 7 ), turbulence error in gust flow field (such as Figure 8 and flow field distortion coefficient (like Figure 9 Analysis revealed that time-domain error and frequency-domain error increased with... The increase shows a trend of first decreasing and then increasing, but the two error amplitudes correspond to... There are differences in direction and location. (By...) Figure 7 It can be seen that the frequency domain error mainly comes from the overband. Figure 8 The turbulence error shown varies with The error initially remains relatively constant before gradually increasing, which differs from the trend of the other two types of errors. The final calculated flow field distortion coefficient is as follows: Figure 9 As shown, in The range of -0.1 to 0 exhibits the best uniformity, and Figure 5 The observed phenomena are consistent. This method can reflect the homogeneity of the flow field and quickly identify the region with minimal distortion and optimal homogeneity. Flow field distortion coefficient. It can also be extended to other types of gust flow fields.

[0085] By implementing the above steps, this application replaces the traditional evaluation method that relies on manual experience to select a small number of characteristic parameters with two comprehensive indicators: waveform distortion coefficient and flow field distortion coefficient. This eliminates subjective judgment differences and makes the evaluation results comparable across different experiments, operators, and experimental conditions. In waveform quality evaluation, this application considers both the time domain and frequency domain dimensions. In flow field uniformity evaluation, this application further considers three factors: time domain, frequency domain, and turbulent disturbance, overcoming the technical shortcomings of existing methods that cannot comprehensively reflect waveform distortion and flow field inhomogeneity. Furthermore, this application is not limited to specific waveform types (sine, 1-cos, etc.) and can be applied to both single-point velocity data and velocity field data.

[0086] The gust waveform and flow field evaluation method provided in this embodiment can be applied to wind tunnel gust test scenarios. This scenario includes a test preparation phase, a test execution phase, and a data processing phase. Test personnel set target gust parameters (such as waveform type, gust ratio, frequency, etc.) according to test requirements, entering the test preparation phase; the gust generator parameters are initialized using the system described in this application, entering the test execution phase; the gust generator generates an unsteady flow field, and the data acquisition system acquires velocity field data within the wind tunnel test section, entering the data processing phase. The gust waveform and flow field evaluation method provided in this embodiment belongs to the quantitative evaluation and feedback optimization steps in the data processing phase. Specifically, in the data processing process for gust tests, the gust waveform quality and flow field spatial uniformity can be quantitatively evaluated based on the waveform distortion coefficient and flow field distortion coefficient. Based on the evaluation results, the amplitude and motion period of the gust generator can be automatically adjusted, or an effective test area meeting quality requirements can be automatically identified, thereby achieving closed-loop control and intelligent optimization of the gust test.

[0087] The control system for gust waveform and flow field provided in this application includes: The parameter input module is used to set the amplitude and motion cycle of the gust generator as adjustable control parameters.

[0088] The gust evaluation module is used to calculate the waveform distortion coefficient based on the gust waveform and flow field evaluation method described above.

[0089] The parameter iterative adjustment module is used to iteratively adjust the amplitude and motion period based on the deviation between the waveform distortion coefficient and the preset target range, until both the waveform distortion coefficient and the gust ratio meet the preset target range.

[0090] The flow field evaluation module is used to calculate the gust flow field distortion coefficient based on the above-described evaluation method of gust waveform and flow field after the parameters meet the preset target range.

[0091] The region identification module is used to identify effective spatial regions that meet the distortion constraints based on the gust flow field distortion coefficient.

[0092] Specifically, to achieve quantitative control of gust quality, a gust generator parameter adjustment system was established, the flowchart of which is shown below. Figure 10 As shown, the operation process is as follows: First, set the target parameter: gust ratio range. Maximum waveform distortion coefficient and maximum flow field distortion coefficient .

[0093] Adjustable control parameters for initializing the gust generator: maximum rotation angle and cycle T。

[0094] Start the gust generator to generate gust waveforms and flow fields, collect data, and select a point waveform. and selected range flow field .

[0095] The collected data is processed to calculate the gust waveform evaluation index (gust ratio). GR Waveform distortion coefficient ).

[0096] Judging the gust ratio GR and waveform distortion coefficient Does the objective meet? and .

[0097] If the target is not met, return to perform parameter feedback adjustment and update the gust generator parameters (maximum turning angle). and cycle T ), and regenerate the gust waveform and flow field.

[0098] If the objective is met, then the flow field distortion coefficient, an evaluation index for gust flow field, is further calculated. .

[0099] Based on the flow field distortion coefficient Spatial regions were selected to meet the requirements. Effective space area S .

[0100] Output maximum turning angle ,cycle T Gusts GR, Waveform distortion coefficient 、 Flow field distortion coefficient and effective space area S .

[0101] The system uses the amplitude of the gust generator, which is the maximum turning angle. A m and exercise cycle T As an adjustable control parameter, the waveform distortion coefficient is calculated as a feedback evaluation index to iteratively adjust the parameters of the gust generator, ensuring that both the gust ratio and waveform distortion coefficient of the generated gusts meet the preset target range. After the parameters meet the target requirements, the spatial flow field is further evaluated based on the flow field distortion coefficient to identify the effective spatial region that meets the distortion constraint conditions, thereby determining the spatial range within which the gust quality meets the requirements. This method achieves closed-loop regulation of the gust generation process, enabling stable and controllable gust characteristics under given target conditions, and provides a reliable basis for the optimal selection of gust test areas.

[0102] This application, based on the establishment of a quantitative evaluation system for gust waveform and flow field quality, constructs a gust generator parameter feedback adjustment mechanism based on waveform distortion coefficient and flow field distortion coefficient. The gust generator amplitude and motion period are used as adjustable control variables. An iterative optimization strategy is introduced to automatically adjust key parameters, and spatial distortion constraints are combined to screen and determine the flow field region, thus forming a closed-loop control method for the gust generation process. Through these technical means, stable output of gust characteristics within a preset target range is achieved, and the effective spatial region meeting gust quality requirements can be automatically identified, improving the controllability, stability, and repeatability of the gust generation process. This method effectively solves the technical problems in the background technology, such as the reliance on repeated manual experience for gust generator parameter adjustment, low efficiency in the adjustment process, difficulty in ensuring stable and consistent gust quality, and difficulty in accurately determining the effective test area meeting gust quality requirements. Therefore, it significantly improves the automation level and engineering application reliability of the gust testing process.

[0103] This application boasts strong universality. The proposed evaluation method for gust waveform quality and flow field uniformity is not limited to specific waveforms (such as sin gusts and 1-cos gusts) and can be extended to various waveforms such as trapezoidal gusts and step gusts. It is also applicable to different types of gusts, including continuous / discrete and vertical / flow-direction gusts. This invention provides a unified quantitative comparison standard for the distortion degree and flow field disturbance degree of various gust waveforms, effectively improving the shortcomings of traditional methods, such as poor applicability, reliance on empirical evaluation, poor reliability, and lack of systematicity. The two methods included in this invention can be applied to single-point velocity measurement data and velocity field measurement data, respectively. They can be used independently or in combination to construct a comprehensive evaluation system from low to high dimensions. Furthermore, the application scope of this evaluation method is not limited to velocity characteristics but can also be extended to other parameters such as temperature characteristics and vortex characteristics; it can also be applied to different scenarios besides gusts. This application significantly improves the applicability and versatility of the evaluation method, overcoming the shortcomings of existing technologies, such as narrow applicable scenarios and poor universality.

[0104] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 11 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores experimental gust waveforms and flow fields measured in the target area. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for evaluating gust waveforms and flow fields.

[0105] Figure 11 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0106] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0107] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0108] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations and be authorized by the owner of the corresponding device.

[0109] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0110] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0111] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for evaluating gust waveforms and flow fields, characterized in that, The evaluation methods for gust waveforms and flow fields include: Acquire the experimental gust waveform and experimental gust flow field in the target area spatial experimental measurement; When evaluating the experimental gust waveform: The experimental gust waveform was fitted using the least squares method to obtain the ideal gust waveform; Calculate the time-domain error between the experimental gust waveform and the ideal gust waveform; Calculate the frequency domain error between the experimental gust waveform and the ideal gust waveform; Based on the time-domain error and the frequency-domain error, a waveform distortion coefficient is obtained by dynamic weight fusion; the waveform distortion coefficient is used to quantitatively evaluate the degree of distortion of the gust waveform. When evaluating the experimental gust flow field: Based on the experimental gust flow field, gust signals from several measuring points were extracted, and the gust signals from each measuring point were phase-aligned to obtain the phase-aligned gust signals. Calculate the spatial average wave based on the phase-aligned gust signal; Calculate the spatial-temporal error of the experimental gust flow field; Calculate the spatial frequency domain error of the experimental gust flow field; Calculate the turbulence error of the experimental gust flow field; The spatial time domain error, the spatial frequency domain error, and the turbulence error are dynamically weighted and fused to obtain the gust flow field distortion coefficient; the flow field distortion coefficient is used to quantitatively evaluate the spatial uniformity of the gust flow field.

2. The method for evaluating gust waveforms and flow fields according to claim 1, characterized in that, Calculating the frequency domain error between the experimental gust waveform and the ideal gust waveform specifically includes: The experimental gust waveform and the ideal gust waveform are subjected to discrete Fourier transform to obtain the experimental spectrum and the theoretical spectrum, respectively. The frequency range is divided into low-frequency band, transition band, and high-frequency band. The error between the experimental spectrum and the theoretical spectrum in each frequency band is calculated to obtain the low-frequency band error, transition band error, and high-frequency band error. The frequency domain error is obtained by summing the low-frequency band error, the transition band error, and the high-frequency band error and taking the square root.

3. The method for evaluating gust waveforms and flow fields according to claim 2, characterized in that, The error expression between the experimental spectrum and the theoretical spectrum in each frequency band is as follows: ； in, This refers to the error in each frequency band within different frequency ranges; Theoretical spectrum; This is the experimental spectrum; For dimensionless frequency, The frequency range for each frequency band; Maximum frequency; The frequency domain error expression is as follows: ； in, For frequency domain error; Low-frequency band error; This is due to excessive bandwidth error; This is for high-frequency band error.

4. The method for evaluating gust waveforms and flow fields according to claim 1, characterized in that, The expression for the waveform distortion coefficient is: ； in, The waveform distortion coefficient; Compared to gusts; For frequency domain enhancement coefficients; For time-domain error; This represents the frequency domain error.

5. The method for evaluating gust waveforms and flow fields according to claim 1, characterized in that, The expression for the turbulence error is: ； ； in, This is for turbulence error; For local turbulence intensity; The spatially average turbulence intensity; for The number of discrete points in the direction; for The number of discrete points in the direction.

6. The method for evaluating gust waveforms and flow fields according to claim 1, characterized in that, The expression for the spatial frequency domain error in each frequency band is as follows: ； in, Spatial error in each frequency band within different frequency ranges; The spectrum obtained from the gust signal; The spectrum obtained from the spatially averaged wave; For dimensionless frequency, The frequency range for each frequency band; Maximum frequency; The spatial frequency domain error expression is: ； in, This refers to spatial frequency domain error; This refers to low-frequency band space error. For spatial overband error; This refers to the high-frequency band error in space.

7. The method for evaluating gust waveforms and flow fields according to claim 1, characterized in that, The expression for the flow field distortion coefficient is: ； ； ； ； in, The flow field distortion coefficient; This refers to spatial-temporal errors; The weights for spatial-temporal errors; This refers to spatial frequency domain error; The weights for spatial frequency domain errors; This is for turbulence error; The weights for turbulence errors; and For weight adjustment parameters; This represents the gust ratio of the space-averaged wave.

8. A control system for gust waveforms and flow fields, characterized in that, The control system for the gust waveform and flow field includes: The parameter input module is used to set the amplitude and motion cycle of the gust generator as adjustable control parameters. A gust evaluation module is used to calculate the waveform distortion coefficient based on the gust waveform and flow field evaluation method according to any one of claims 1-7; The parameter iterative adjustment module is used to iteratively adjust the amplitude and motion period according to the deviation between the waveform distortion coefficient and the preset target range until both the waveform distortion coefficient and the gust ratio meet the preset target range. The flow field evaluation module is used to calculate the gust flow field distortion coefficient based on the gust waveform and flow field evaluation method described in any one of claims 1-7 after the parameters meet the preset target range. The region identification module is used to identify effective spatial regions that meet the distortion constraints based on the gust flow field distortion coefficient.

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